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Robot-assisted neurocognitive rehabilitation of the hand

Robot-assisted neurocognitive rehabilitation of the hand

Authors: Irina Benedek, Oana Vanta

Keywords: Neurorehabilitation, subacute stroke, hand, robotics


Innovations in neurorehabilitation of the upper limbs

Is robot-assisted neurocognitive rehabilitation of the hand effective? Stroke is the leading cause of mortality and disability worldwide, negatively impacting the overall quality of life of patients. Most stroke survivors suffer from different types of motor impairments, despite the progressions in the field. Since neurorehabilitation of the upper limbs remains challenging, robotic-assisted therapies have been studied and developed in recent years. These methods were established as safe and practical therapies were used additionally to neurorehabilitation programs [1]. More precisely, in the last two decades, robotic devices that focused on training the proximal upper extremity were assessed, showing results similar to dose-matched conventional therapies [2]. 

For more information on upper limb neurorehabilitation, visit:

A multimodal approach to robot-assisted neurocognitive rehabilitation of the hand

Distal arm sensorimotor function is essential for improving the quality of life after a stroke. Nevertheless, the distal arm function is crucial for the ability to perform daily activities and is usually severely affected after a cerebrovascular accident when there is a low probability of regaining its full function [3]. However, scientific data demonstrates the possibility of recovery with intensive motor training provided by robotic-assisted devices [4, 5]. Until now, the focus has been on movement practice without implementing a therapeutic paradigm adapted to the capabilities of the technology in question.  

In the study by Ranzani et al. [6] from 2020, the goal was to investigate sensorimotor robotic-assisted rehabilitation of hand function and cognition training in patients with subacute stroke. The researchers based their comprehensive approach on the fact that cognition is essential for appropriate interactions between body and environment, such as [6]:

Grafic 01 Importance of Cognition in body environment interactions scaled

Figure 1. Importance of Cognition

Secondary objectives derived from the hypothesis are that neurocognitive robotic-assisted hand recovery would also improve motor, sensory, and cognitive functions in this category of patients. This method is particularly relevant for hand rehabilitation due to the importance of the cognitive processing of sensory information. Furthermore, combining multimodal inputs requires the involvement of associative cortices that are essential for learning and, consequently, neuronal plasticity and recovery [7]. Even though few studies have compared neurocognitive therapies to other rehabilitation treatments [8, 9], preliminary evidence suggested promising results in the following:

  • Enhancing upper limb function
  • Improving the ability to conduct daily tasks
  • The overall quality of life [8,9].

Inspired by the neurocognitive approach, the authors [6] implemented this concept on a robotic-assisted device focused on exercises including grasping and pronation-supination. Virtual objects were produced by the robot, both visually and haptically, simulating the tangible materials used in traditional recovery [6]. 

Study design 

The study conducted by Ranzani et al. [6] was a randomized control trial that took place in Switzerland. Patients were randomly assigned into two groups: the robot-assisted group (RG), which benefited from neurocognitive therapy with “ReHapticKnob” (Fig. 2), and the control group (CG), which received dose-matched classical neurocognitive therapy. With the main focus on hand function, all participants had three neurocognitive therapy sessions per day over four weeks. Each session lasted 45 minutes, and in the RG, one session was replaced by robot-assisted therapy. The subjects were under the careful supervision of a specialized therapist who adapted the intensity and difficulty level of the exercises according to every patient. The type and number of exercises performed in the RG group were also assessed to match the therapy type and dose performed in conventional recovery programs [6]. 




12984 2020 746 Fig1 HTML

Figure 2. Picture of a subject with stroke, using the rehabilitation robot ( available from [6])
Between April 2013 and March 2017, 33 patients who suffered a subacute stroke were evaluated and included in the study. Seventeen subjects were randomized in the RG, while 16 were allocated to the CG. 27 patients benefited from the allocated therapy and completed the T1 assessment, while 23 completed the entire protocol. Six patients withdrew before the T1 evaluation due to medical reasons or lack of motivation. No adverse events related to the interventions were noted [6]. The eligibility criteria are described in Figure 3 below:
Grafic 02 Patient eligibility

Figure 3. Patient Eligibility

The roles of robotic devices in improving upper extremity function

As mentioned above, the neurocognitive method proposed in this study implied both sensorimotor and cognitive components, which are equally important during the performance of complicated tasks and daily activities. Patients were instructed to explore different objects, such as sponges, sticks, and strings, to discern their properties by using haptic and postural senses. A robotic device is an ideal therapeutic instrument for performing such tasks because it can render a complex range of stimuli in a repeatable, well-controlled manner [10]. 

The neurorehabilitation program provided  in the RG group consisted of  seven types of exercises [6], as showcased in Figure 4 below:

Grafic 03 Exercises provided in robot assisted group scaled

Figure 4. Types of Exercises


The motor aspects of the intervention included symmetric thumb and finger flexion/extension and forearm pronation/supination, executed separately or combined. The sensory aspects included encoding the following types of somatosensory signals without visual information: sponge/spring stiffness, object shape and size, arm positioning, and vibratory cues. The cognitive element of the training, executed passively (with guidance from the robot or therapist) or actively (by the subject), required the elaboration/recognition of perceptual information (object length, stiffness) and encoding/decoding of this information in working memory for comparison purposes of more than one item, along with planning and executing the specific motor plans [6].  

The study’s primary outcome was evaluating the changes in upper limb extremity motor impairment from baseline to the end of treatment (T0-T1). The parameter used to quantify the results was the Fugl-Meyer Assessment of the Upper Limb Extremity scale (FMA-UE). Different motor, sensory and cognitive scales were also used for the secondary outcomes. The average number of task repetitions and therapy intensity were assessed to compare the two study groups in terms of dose matching. For the acceptance of the neurocognitive robotic-assisted program, a 4-item questionnaire was developed. The two study groups were evaluated and compared for all outcome measures after the intervention (T-T0) and at the regular follow-ups (T2-T0 and T3-T0) [6]. 

Results, discussions, and future perspectives

According to the equivalence analysis, the change in FMA-UE in the robotic-assisted group may be regarded as non-inferior to the control group. Both groups showed improvements in all secondary clinical scores after 4 weeks of therapy (T1). In addition to motor impairments, sensory and cognitive deficits were also improved in both groups after 4 weeks of treatment (T1) [6]. However, by the end of the study, subjects randomized in the RG group tended to show better results. At T2 and T3, the changes in FMA-UE were also sustained. When comparing changes in clinical measures over time, the two groups did not demonstrate any significant between-group differences [6]. 

According to the supervising therapist, the RG did an average of 71.49 task repetitions during a treatment session, compared to 73.47 in the Control Group. There was no statistically significant difference in therapy intensity between the two groups when comparing robot-assisted and traditional therapy sessions [6]. 

Regarding neurocognitive therapy sessions, the average daily quantity of occupational therapy and/or lower limb kinesiotherapy did not differ statistically between the groups. 

The acceptance of the neurocognitive robot-assisted therapy was evaluated by using a self-reported questionnaire answered by 12 patients [6], as showcased in Figure 5:

Grafic 04 Responses of patients scaled

Figure 5. Patient responses


Unlike other robot-assisted rehabilitation studies that focus primarily on locomotor training, this method fully utilized the robot’s haptic rendering capabilities and suggested a therapy regimen tailored to this potential [6].

Ranzani and the team were able to demonstrate that this strategy was well-liked and recommended by the majority of patients. It could also be included in the patient’s daily routines in the subacute stage of stroke recovery. Although the questionnaire pointed out slight pain in finger fixation and difficulty levels were sometimes evaluated as excessively high in three out of seven exercises [6], most subjects considered the program encouraging and pleasant, and they noticed tangible benefits to their health after completing it. 

The findings of the equivalency test assessing the evolution in the FMA-UE show that motor recovery in the RG was not inferior to the CG for the specific intervention. Usually, little to no difference between traditional and robotic-assisted therapies should be expected since the dosage and therapeutic movements are meant to be identical between the study groups. Furthermore, the idea that a traditional treatment session might be replaced without compromising the overall rehab programs opens up new possibilities for robot-assisted therapy development [6]. 

Conclusion on robot-assisted neurocognitive rehabilitation of the hand

This research reported the findings of a randomized controlled trial that aimed to compare robotic-assisted recovery to conventional therapy for patients with a subacute stroke and impairment of hand function. Although the study included a limited number of participants, the results showed that robot-assisted treatments might be successfully incorporated into clinical neurorehabilitation routines [6]. In other words, this trial is also an indicator of how low data can become significant data. Compared to conventional dose-matched neurocognitive programs, the results demonstrated that this innovative approach was at least as practical as the conventional one. 

Early exposure of stroke patients to using such patient-tailored robot-assisted therapy programs opens the door to using such technology in the clinic with little therapist supervision or at home after hospital release to help enhance the dose of hand therapy for stroke patients [6].


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  2. Maciejasz P, Eschweiler J, Gerlach-Hahn K, Jansen-Troy A, Leonhardt S. A survey on robotic devices for upper limb rehabilitation. J Neuroeng Rehabil. 2014;11(1):3.  DOI: 10.1186/1743-0003-11-3
  3. Fischer HC, Stubblefield K, Kline T, Luo X, Kenyon RV, Kamper DG. Hand rehabilitation following stroke: a pilot study of assisted finger extension training in a virtual environment. Top Stroke Rehabil. 2007;14(1):1–12. DOI: 10.1310/tsr1401-1
  4. Lambercy O, Dovat L, Yun H, Wee SK et al. Effects of a robot-assisted training of grasp and pronation/supination in chronic stroke: a pilot study. J Neuroeng Rehabil. 2011;8(1):63. doi: 10.1186/1743-0003-8-63.
  5. Hsieh YW, Lin KC, Wu CY, Shih TY, Li MW, Chen CL. Comparison of proximal versus distal upper-limb robotic rehabilitation on motor performance after stroke: a cluster controlled trial. Sci Rep. 2018;8(1):2091. doi: 10.1038/s41598-018-20330-3
  6. Ranzani R, Lambercy O, Metzger JC, Califfi A et al. Neurocognitive robot-assisted rehabilitation of hand function: a randomized control trial on motor recovery in subacute stroke. J Neuroeng Rehabil. 2020 Aug 24;17(1):115. doi: 10.1186/s12984-020-00746-7. 
  7. Van de Winckel A, Wenderoth N, De Weerdt W, Sunaert S et al. Frontoparietal involvement in passively guided shape and length discrimination: a comparison between subcortical stroke patients and healthy controls. Exp Brain Res Springer. 2012;220(2):179–89. doi: 10.1007/s00221-012-3128-2
  8. Sallés L, Martín-Casas P, Gironès X, Durà MJ et al. A neurocognitive approach for recovering upper extremity movement following subacute stroke: a randomized controlled pilot study. J Phys Ther Sci. 2017;29(4):665–72. doi: 10.1589/jpts.29.665
  9. Lee S, Bae S, Jeon D, Kim KY. The effects of cognitive exercise therapy on chronic stroke patients’ upper limb functions, activities of daily living and quality of life. J Phys Ther Sci. 2015;27(9):2787–91. doi: 10.1589/jpts.27.2787
  10. Metzger J-C, Lambercy O, Califfi A, Dinacci D et al. Assessment-driven selection and adaptation of exercise difficulty in robotassisted therapy: a pilot study with a hand rehabilitation robot. J Neuroeng Rehabil. 2014;11(1):154. Available at:

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